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Article
Publication date: 4 September 2019

S. Khodaygan and A. Ghaderi

The purpose of this paper is to present a new efficient method for the tolerance–reliability analysis and quality control of complex nonlinear assemblies where explicit…

Abstract

Purpose

The purpose of this paper is to present a new efficient method for the tolerance–reliability analysis and quality control of complex nonlinear assemblies where explicit assembly functions are difficult or impossible to extract based on Bayesian modeling.

Design/methodology/approach

In the proposed method, first, tolerances are modelled as the random uncertain variables. Then, based on the assembly data, the explicit assembly function can be expressed by the Bayesian model in terms of manufacturing and assembly tolerances. According to the obtained assembly tolerance, reliability of the mechanical assembly to meet the assembly requirement can be estimated by a proper first-order reliability method.

Findings

The Bayesian modeling leads to an appropriate assembly function for the tolerance and reliability analysis of mechanical assemblies for assessment of the assembly quality, by evaluation of the assembly requirement(s) at the key characteristics in the assembly process. The efficiency of the proposed method by considering a case study has been illustrated and validated by comparison to Monte Carlo simulations.

Practical implications

The method is practically easy to be automated for use within CAD/CAM software for the assembly quality control in industrial applications.

Originality/value

Bayesian modeling for tolerance–reliability analysis of mechanical assemblies, which has not been previously considered in the literature, is a potentially interesting concept that can be extended to other corresponding fields of the tolerance design and the quality control.

Details

Assembly Automation, vol. 39 no. 5
Type: Research Article
ISSN: 0144-5154

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Article
Publication date: 5 July 2018

Harindranath R.M. and Jayanth Jacob

This paper aims to popularize the Bayesian methods among novice management researchers. The paper interprets the results of Bayesian method of confirmatory factor analysis

Abstract

Purpose

This paper aims to popularize the Bayesian methods among novice management researchers. The paper interprets the results of Bayesian method of confirmatory factor analysis (CFA), structural equation modelling (SEM), mediation and moderation analysis, with the intention that the novice researchers will apply this method in their research. The paper made an attempt in discussing various complex mathematical concepts such as Markov Chain Monte Carlo, Bayes factor, Bayesian information criterion and deviance information criterion (DIC), etc. in a lucid manner.

Design/methodology/approach

Data collected from 172 pharmaceutical sales representatives were used. The study will help the management researchers to perform Bayesian CFA, Bayesian SEM, Bayesian moderation analysis and Bayesian mediation analysis using SPSS AMOS software.

Findings

The interpretation of the results of Bayesian CFA, Bayesian SEM and Bayesian mediation analysis were discussed.

Practical implications

The management scholars are non-statisticians and are not much aware of the benefits offered by Bayesian methods. Hitherto, the management scholars use predominantly traditional SEM in validating their models empirically, and this study will give an exposure to “Bayesian statistics” that has practical advantages.

Originality/value

This is one paper, which discusses the following four concepts: Bayesian method of CFA, SEM, mediation and moderation analysis.

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Article
Publication date: 21 February 2018

Franz T. Lohrke, Charles M. Carson and Archie Lockamy

The purpose of this paper is to review Bayesian analysis in recent entrepreneurship research to assess how scholars have employed these methods to study the…

Abstract

Purpose

The purpose of this paper is to review Bayesian analysis in recent entrepreneurship research to assess how scholars have employed these methods to study the entrepreneurship process. Researchers in other business fields (e.g. management science, marketing, and finance) have increasingly employed Bayesian methods to study issues like decision making. To date, however, Bayesian methods have seen only limited use in entrepreneurship research.

Design/methodology/approach

After providing a general overview of Bayesian methods, this study examines how extant entrepreneurship research published in leading journals has employed Bayesian analysis and highlights topics these studies have investigated most frequently. It next reviews topics that scholars from other business disciplines have investigated using these methods, focusing on issues related to decision making, in particular.

Findings

Only seven articles published in leading management and entrepreneurship journals between 2000 and 2016 employed or discussed Bayesian methods in depth when studying the entrepreneurship process. In addition, some of these studies were conceptual.

Research limitations/implications

This review suggests that Bayesian methods may provide another important tool for researchers to employ when studying decision making in high uncertainty situations or the impact of entrepreneurial experience on decision making over time.

Originality/value

This review demonstrates that Bayesian analysis may be particularly appropriate for entrepreneurship research. By employing these methods, scholars may gain additional insights into entrepreneurial phenomenon by allowing researchers to examine entrepreneurial decision making. Through this review and these recommendations, this study hopes to encourage greater Bayesian analysis usage in future entrepreneurship research.

Details

Management Decision, vol. 56 no. 5
Type: Research Article
ISSN: 0025-1747

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Book part
Publication date: 1 January 2008

Arnold Zellner

After briefly reviewing the past history of Bayesian econometrics and Alan Greenspan's (2004) recent description of his use of Bayesian methods in managing policy-making…

Abstract

After briefly reviewing the past history of Bayesian econometrics and Alan Greenspan's (2004) recent description of his use of Bayesian methods in managing policy-making risk, some of the issues and needs that he mentions are discussed and linked to past and present Bayesian econometric research. Then a review of some recent Bayesian econometric research and needs is presented. Finally, some thoughts are presented that relate to the future of Bayesian econometrics.

Details

Bayesian Econometrics
Type: Book
ISBN: 978-1-84855-308-8

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Article
Publication date: 2 September 2019

Pedro Albuquerque, Gisela Demo, Solange Alfinito and Kesia Rozzett

Factor analysis is the most used tool in organizational research and its widespread use in scale validations contribute to decision-making in management. However, standard…

Abstract

Purpose

Factor analysis is the most used tool in organizational research and its widespread use in scale validations contribute to decision-making in management. However, standard factor analysis is not always applied correctly mainly due to the misuse of ordinal data as interval data and the inadequacy of the former for classical factor analysis. The purpose of this paper is to present and apply the Bayesian factor analysis for mixed data (BFAMD) in the context of empirical using the Bayesian paradigm for the construction of scales.

Design/methodology/approach

Ignoring the categorical nature of some variables often used in management studies, as the popular Likert scale, may result in a model with false accuracy and possibly biased estimates. To address this issue, Quinn (2004) proposed a Bayesian factor analysis model for mixed data, which is capable of modeling ordinal (qualitative measure) and continuous data (quantitative measure) jointly and allows the inclusion of qualitative information through prior distributions for the parameters’ model. This model, adopted here, presents considering advantages and allows the estimation of the posterior distribution for the latent variables estimated, making the process of inference easier.

Findings

The results show that BFAMD is an effective approach for scale validation in management studies making both exploratory and confirmatory analyses possible for the estimated factors and also allowing the analysts to insert a priori information regardless of the sample size, either by using the credible intervals for Factor Loadings or by conducting specific hypotheses tests. The flexibility of the Bayesian approach presented is counterbalanced by the fact that the main estimates used in factor analysis as uniqueness and communalities commonly lose their usual interpretation due to the choice of using prior distributions.

Originality/value

Considering that the development of scales through factor analysis aims to contribute to appropriate decision-making in management and the increasing misuse of ordinal scales as interval in organizational studies, this proposal seems to be effective for mixed data analyses. The findings found here are not intended to be conclusive or limiting but offer a useful starting point from which further theoretical and empirical research of Bayesian factor analysis can be built.

Details

RAUSP Management Journal, vol. 54 no. 4
Type: Research Article
ISSN: 2531-0488

Keywords

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Article
Publication date: 7 June 2021

Carol K.H. Hon, Chenjunyan Sun, Bo Xia, Nerina L. Jimmieson, Kïrsten A. Way and Paul Pao-Yen Wu

Bayesian approaches have been widely applied in construction management (CM) research due to their capacity to deal with uncertain and complicated problems. However, to…

Abstract

Purpose

Bayesian approaches have been widely applied in construction management (CM) research due to their capacity to deal with uncertain and complicated problems. However, to date, there has been no systematic review of applications of Bayesian approaches in existing CM studies. This paper systematically reviews applications of Bayesian approaches in CM research and provides insights into potential benefits of this technique for driving innovation and productivity in the construction industry.

Design/methodology/approach

A total of 148 articles were retrieved for systematic review through two literature selection rounds.

Findings

Bayesian approaches have been widely applied to safety management and risk management. The Bayesian network (BN) was the most frequently employed Bayesian method. Elicitation from expert knowledge and case studies were the primary methods for BN development and validation, respectively. Prediction was the most popular type of reasoning with BNs. Research limitations in existing studies mainly related to not fully realizing the potential of Bayesian approaches in CM functional areas, over-reliance on expert knowledge for BN model development and lacking guides on BN model validation, together with pertinent recommendations for future research.

Originality/value

This systematic review contributes to providing a comprehensive understanding of the application of Bayesian approaches in CM research and highlights implications for future research and practice.

Details

Engineering, Construction and Architectural Management, vol. ahead-of-print no. ahead-of-print
Type: Research Article
ISSN: 0969-9988

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Article
Publication date: 29 November 2019

A. George Assaf and Mike G. Tsionas

This paper aims to present several Bayesian specification tests for both in- and out-of-sample situations.

Abstract

Purpose

This paper aims to present several Bayesian specification tests for both in- and out-of-sample situations.

Design/methodology/approach

The authors focus on the Bayesian equivalents of the frequentist approach for testing heteroskedasticity, autocorrelation and functional form specification. For out-of-sample diagnostics, the authors consider several tests to evaluate the predictive ability of the model.

Findings

The authors demonstrate the performance of these tests using an application on the relationship between price and occupancy rate from the hotel industry. For purposes of comparison, the authors also provide evidence from traditional frequentist tests.

Research limitations/implications

There certainly exist other issues and diagnostic tests that are not covered in this paper. The issues that are addressed, however, are critically important and can be applied to most modeling situations.

Originality/value

With the increased use of the Bayesian approach in various modeling contexts, this paper serves as an important guide for diagnostic testing in Bayesian analysis. Diagnostic analysis is essential and should always accompany the estimation of regression models.

Details

International Journal of Contemporary Hospitality Management, vol. 32 no. 4
Type: Research Article
ISSN: 0959-6119

Keywords

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Article
Publication date: 29 April 2021

Chaochao Liu, Zhanwen Niu and Qinglin Li

Existing studies suggested that there is a nonlinear relationship between lean production adoption and organizational performance. Lean production adoption is a gradual…

Abstract

Purpose

Existing studies suggested that there is a nonlinear relationship between lean production adoption and organizational performance. Lean production adoption is a gradual process, and the application status of lean tools will affect enterprise performance. The existing literature has insufficiently explored the nonlinear relationship of the lean tools application status on operational performance and environmental performance using the same theoretical framework. A combination approach of interpretative structural modeling (ISM) and Bayesian networks was proposed in this paper, which was used to analyze the complex relationship between lean tools application status with operational and environmental performance.

Design/methodology/approach

ISM was used to analyze the inter-relationship of 17 lean tools identified from the lean literature and construct the lean tools structure model providing reference for building Bayesian network. By calculating the prior and conditional probabilities within the lean tools and between the lean tools with the operational and environmental performance, a Bayesian simulation model was constructed and used to analyze the performance outcomes under different lean tools application status.

Findings

The performance simulation result – representing by the probability of three performance levels as good, average and poor – shows inconsistent changes with the changing of lean tools application status. By comparing the changes of operational performance and environmental performance, it can be found that environmental performance is less sensitive to the change of lean tools application status than operational performance.

Originality/value

Using the integrated ISM–Bayesian network approach, the results indicated a nonlinear relationship between lean tools with operational and environmental performance and provided a reference for the exploration of the nonlinear relationship between lean tools and performance. This research further calls for exploring the S-curve relationship between lean tools and environmental performance.

Details

The TQM Journal, vol. ahead-of-print no. ahead-of-print
Type: Research Article
ISSN: 1754-2731

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Article
Publication date: 11 September 2017

Arvind Shrivastava, Nitin Kumar and Purnendu Kumar

Decisions pertaining to working capital management have pivotal role for firms’ short-term financial decisions. The purpose of this paper is to examine impact of working…

Abstract

Purpose

Decisions pertaining to working capital management have pivotal role for firms’ short-term financial decisions. The purpose of this paper is to examine impact of working capital on profitability for Indian corporate entities.

Design/methodology/approach

Both classical panel analysis and Bayesian techniques have been employed that provides opportunity not only to perform comparative analysis but also allows flexibility in prior distribution assumptions.

Findings

It is found that longer cash conversion period has detrimental influence on profitability. Financial soundness indicators are playing significant role in determining firm profitability. Larger firms seem to be more profitable and significant as per Bayesian approach. Bayesian approach has led to considerable gain in estimation fit.

Practical implications

Observing the highly skewed distribution of dependent variable, Multivariate Student t-distribution has been considered along with normal distribution to model stochastic term. Accordingly, Bayesian methodology is applied.

Originality/value

Analysis of working capital for firms has been performed in Indian context. Application of Bayesian methodology is performed on balanced panel spanning from 2003 to 2012. As per author’s knowledge, this is the first study which applies Bayesian approach employing panel data for the analysis of working capital management for Indian firms.

Details

Journal of Economic Studies, vol. 44 no. 4
Type: Research Article
ISSN: 0144-3585

Keywords

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Book part
Publication date: 1 January 2008

Michiel de Pooter, Francesco Ravazzolo, Rene Segers and Herman K. van Dijk

Several lessons learnt from a Bayesian analysis of basic macroeconomic time-series models are presented for the situation where some model parameters have substantial…

Abstract

Several lessons learnt from a Bayesian analysis of basic macroeconomic time-series models are presented for the situation where some model parameters have substantial posterior probability near the boundary of the parameter region. This feature refers to near-instability within dynamic models, to forecasting with near-random walk models and to clustering of several economic series in a small number of groups within a data panel. Two canonical models are used: a linear regression model with autocorrelation and a simple variance components model. Several well-known time-series models like unit root and error correction models and further state space and panel data models are shown to be simple generalizations of these two canonical models for the purpose of posterior inference. A Bayesian model averaging procedure is presented in order to deal with models with substantial probability both near and at the boundary of the parameter region. Analytical, graphical, and empirical results using U.S. macroeconomic data, in particular on GDP growth, are presented.

Details

Bayesian Econometrics
Type: Book
ISBN: 978-1-84855-308-8

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